Detection Of Intrusion Attacks Using Neural Networks

Abstract

The rapid expansion of computer networks makes security issues among computer systems one of the most important. Intrusion detection systems are using artificial intelligence more and more. This article discusses intrusion detection. Multi-layer perceptron (MLP) is used to detect offline intrusion attacks. The work uses the issues of determining the type of attack. Various neural network structures are considered to detect the optimal neural network by the number of input neurons and the number of hidden layers. It has also been investigated that activation functions and their influence on increasing the ability to generalize a neural network. The results show that the neural network is a 15x31x1 way to classify records with an accuracy of about 99% for known types of attacks, with an accuracy of 97% for normal vectors and 34% for unknown types of attacks.